where the Lagrangian rate-distortion functional \(J\)  is minimized for a particular value of the Lagrange multiplier. More details on Lagrangian optimization are discussed in [1]}. We go beyond the traditional hybrid video coding framework by building up our compression pipeline upon the learning based codecs, in which the modules can be jointly optimized for better implementing R-D optimization. We attempt to define the pipeline of video coding for machine (VCM) to bridge the gap between coding semantic features for machine vision tasks and coding pixel features for human vision.
